{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T04:45:41Z","timestamp":1775969141292,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>While predictive accuracy is often prioritized in machine learning (ML) models, interpretability remains essential in scientific and high-stakes domains. However, diverse interpretability algorithms frequently yield conflicting explanations, highlighting the need for consensus to harmonize results. In this study, six ML models were trained on six synthetic datasets with known ground truths, utilizing various model-agnostic interpretability techniques, as well as gradient-based and counterfactual-based explainers. Consensus explanations were generated using established methods and a novel approach: WISCA (Weighted Scaled Consensus Attributions), which integrates class probability and normalized attributions. WISCA consistently aligned with the most reliable individual method, underscoring the value of robust consensus strategies in improving explanation reliability.<\/jats:p>","DOI":"10.3390\/make8040097","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T12:01:52Z","timestamp":1775822512000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1158-8877","authenticated-orcid":false,"given":"Antonio Jes\u00fas","family":"Banegas-Luna","sequence":"first","affiliation":[{"name":"Departamento de Tecnolog\u00edas de la Informaci\u00f3n y Telecomunicaciones, Centro Universitario de la Defensa, Academia General del Aire, Universidad Polit\u00e9cnica de Cartagena, C\/Coronel L\u00f3pez Pe\u00f1a s\/n, 30729 San Javier, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4468-7898","authenticated-orcid":false,"given":"Horacio","family":"P\u00e9rez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics and High Performance Computing (BIO-HPC), Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5029-3089","authenticated-orcid":false,"given":"Carlos","family":"Mart\u00ednez-Cort\u00e9s","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics and High Performance Computing (BIO-HPC), Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qu, K., Guo, F., Liu, X., Lin, Y., and Zou, Q. 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